Founder, Datooine

Thibault Djaballah

Fractional CTO and ML & AI engineer. Since 2017 I’ve been building machine learning models, data pipelines and Python backends, from data science at Renault to Criteo’s bidding models, and now independently through Datooine.

Talk about your project

About me

An engineering graduate of the École des Mines de Saint-Étienne, I started in data science at KPMG and then Renault, before three years of machine learning at Criteo, working on the models that price every ad impression.

After the lockdown I left Paris for Montpellier and founded Datooine Technologies. Through it I bring that experience to startups and SMEs: as a fractional CTO when they need technical leadership, as an ML & AI engineer when they need to ship.

What I care about: simple, tested systems that someone else can take over. And AI where it genuinely adds something.

Outside of code, I’m into cinema, badminton and triathlon.

Skills

An overview of what I can put in place for your project.

Areas

  • Machine learning
  • LLMs and AI agents
  • Data engineering
  • MLOps
  • Web API development
  • Hexagonal architecture
  • DevOps
  • Big data
  • Data visualization
  • Technical mentoring

Languages & frameworks

  • Python
  • TypeScript
  • SQL
  • Scala
  • FastAPI
  • PydanticAI
  • TensorFlow
  • Apache Spark
  • dbt
  • MongoDB
  • Hive, Hadoop

Cloud & infrastructure

  • Amazon Web Services (AWS)
  • Amazon Bedrock AgentCore
  • Google Cloud Platform (GCP)
  • Microsoft Azure
  • Microsoft Fabric
  • Docker, Kubernetes
  • Terraform
  • GitHub Actions

BI tools

  • Microsoft Power BI
  • Tableau

Experience

Data Engineer
Urbasolar (via Datooine Technologies), Montpellier, FR
September 2026 - Present

Built an ELT pipeline and dbt transformations in Microsoft Fabric.

Machine Learning Engineer & Software Engineer
Trackit (via Datooine Technologies), Montpellier, FR
June 2026 - Present

Trackit is an AWS partner: every project is built on AWS.

  • Engagements for several clients: Nexspec, AMGI Studio, The Social Department.
  • Backend development, machine learning and automation, depending on each client’s needs.

Software Engineer
Schlumberger (via Datooine Technologies), Montpellier, FR
2022 - April 2026

Built a Python backend that estimates the CO2-equivalent emissions of Schlumberger projects.

  • Analysed the client’s goals and clarified the requirements for an MVP.
  • Set up the CI/CD pipeline on Microsoft Azure.
  • Designed a simple, generic model estimating the carbon cost of, for example, a truck, an oil rig or an artificial lift.
  • Built a FastAPI server in Python backed by MongoDB.
  • Integrated other Schlumberger APIs into the application.

Machine Learning Engineer
Criteo, Paris, FR
2019 - 2022

Joined the team responsible for the bidding models that price ad display opportunities.

  • Continuously improved the bidding models estimating click, visit and sale probabilities.
  • Ran offline experiments to beat our more classical models with TensorFlow models.
  • Computed better audiences for clients with a new embeddings-based approach.
  • Mentored and ramped up new team members.
  • Over the years, became the company-wide point of contact for technical questions about the code our team owned.

Data Scientist
Renault Digital, Boulogne-Billancourt, FR
2017 - 2019

In the data team of Renault’s digital factory, we helped the group’s business units with their data challenges.

  • Built a predictive maintenance and automatic quality-defect detection POC from production machine data, for the Manufacturing department.
  • Built a big data product (Spark backend) to compute and analyse vehicle enrichment (options and equipment) across the group, for management control.
  • Ran several training sessions on data and data science for Renault employees.

Data Analyst
KPMG, Paris La Défense, FR
2017

Final-year internship in the Data Science & Analytics team at KPMG France.

  • Applied classical machine learning algorithms to client datasets and built dashboards presenting the data and its predictions. Customer segmentation was a recurring use case.

Education

Ingénieur Civil des Mines (MSc in Engineering)
École des Mines de Saint-Étienne, Saint-Étienne, FR
2013 - 2016

Big data: data mining, data organisation, mathematical methods for high-dimensional data, information systems for big data
Data science: data analysis, statistical learning, machine learning, computer experiments, optimisation, regression, classification
Business management and finance: financial and management accounting, financial analysis, management simulation, financing policies, M&A

Interdisciplinary Program in Technology, Management and Policy
Seoul National University, Seoul, KR
2016

Methodologies for Technology Management, Economics and Policy
Technology and Commercialization
Agent-Based Modeling of Socio-Economic Complex Systems

Research Master’s in Modelling & Applied Mathematics
Université Jean Monnet, Saint-Étienne, FR
2015 - 2016

Deterministic and statistical approaches: statistical learning of deterministic models, optimisation under uncertainty, financial risk

Want to work together?

As a fractional CTO or as an ML & AI engineer: let’s talk about your context.

Book a call